How to Calculate Maintenance Tech Productivity in Property Management

For at least 20 years, the apartment industry has used a ratio of one team member for every 100 units as the default staffing standard. Some operators start with one technician per 150 to 200 units for new properties, then adjust. One technician per 100 units is the most common starting point. The ratio became the default because it is simple to apply and easy to explain to ownership.

The ratio ignores everything that drives actual workload. Property age is the biggest factor. A property built in the last five years generates fewer HVAC failures, fewer plumbing emergencies, and fewer appliance replacements than a property built 30 years ago. Deferred maintenance compounds the problem. If the previous owner skipped roof repairs or delayed boiler replacements, the new manager inherits a backlog that appears as work order volume rather than in the unit count.

Amenity complexity also affects workload. A property with a pool, fitness center, and clubhouse requires more preventive maintenance and more emergency response than a property with no shared amenities. The ratio treats all units as equal, which means it treats a 100-unit garden-style property the same as a 100-unit high-rise with elevators, a parking garage, and a rooftop deck.

Operators know this. One maintenance tech managing 220 units alone asked how many apartments is too many for one person, with ownership refusing to hire help. Property management groups debate whether 100 units per tech is the right number, with some suggesting separate crews for involved turns. The ratio is a starting point, not a staffing plan.

Work orders completed per day or per week is the primary output metric. It tells you how much work each tech is finishing, not how many units they cover. Track the number of work orders completed each week to see whether volume is rising, falling, or holding steady. Divide completed work orders by the number of working days in the period to get work orders per day.

This metric is not a benchmark. There is no industry standard for how many work orders a tech should complete per day. A property with mostly quick fixes will show higher work order counts than a property with complex HVAC repairs or unit turns. The metric is useful for comparison across your own territories, not for comparison to other companies.

Time-on-property is billable hours divided by total clocked hours. Billable hours are the hours charged to a property for completed work. Total clocked hours are the hours the tech was paid for, including time spent on training, travel, administrative tasks, and breaks. Time-on-property reveals whether paid time is spent on property work or other activities.

A tech with high billable hours and high total hours is spending most of their paid time on property work. A tech with low billable hours and high total hours is spending paid time on non-property work, which may be necessary training or may be wasted time. A tech with high billable hours and low total hours is either very productive or working unpaid overtime.

Hours worked per week is the denominator that replaces the flat 40-hour assumption. Most property managers calculate productivity by assuming every tech works 40 hours per week, 52 weeks per year. That ignores PTO, sick days, holidays, and part-time schedules. Tracking actual hours worked shows whether a tech is available for the full week or only part of it, which changes the staffing calculation.

A tech who works 32 hours per week because of a part-time schedule should not be compared to a tech who works 40 hours per week. A tech who took two weeks of PTO in a month should not be judged by the same work order count as a tech who worked the full month. Actual hours worked per week accounts for these differences.

The work order system is the source for completed work orders, billable hours per work order, and property assignments. Most property management platforms let you run a report filtered by technician, property, date range, and work order status. The report should include the work order number, completion date, technician name, property name, and billable hours. Filter the report to completed work orders only, not open or pending tickets.

The payroll or timecard system is the source for total hours worked, including PTO and sick days. Most payroll systems let you run a report by employee, department, and date range. The report should include the employee name, department, manager, and total hours clocked for the period. Make sure the report includes all paid time, not just billable time.

Reconciling these two systems by technician and date range is the manual workflow most operators use today. Productivity is the biggest property management pain because the data lives in two places and requires manual matching. You pull the work order report, pull the payroll report, then compare total wrench time to total paid hours to calculate time-on-property.

The reconciliation step is necessary because the work order system does not know about PTO and the payroll system does not know about billable hours. The two systems track different things. The work order system tracks work completed and billed to properties. The payroll system tracks hours paid to employees. Time-on-property is the ratio between them.

The matching key is the technician name or employee ID. Make sure the names match exactly between the two systems, or use employee ID if both systems support it. Mismatched names will break the reconciliation. If one system uses first name last name and the other uses last name comma first name, you will need to normalize the names before joining the data.

Step one is to sum completed work orders per tech over the date range, then divide by working days to get work orders per day. If a tech completed 60 work orders in a 20-working-day month, that is 3 work orders per day. If another tech completed 40 work orders in the same month, that is 2 work orders per day. The first tech is handling higher volume.

Step two is to sum billable hours and total clocked hours per tech, then divide billable by total to get time-on-property percentage. If a tech billed 120 hours to properties and clocked 150 total hours, time-on-property is 80 percent. If another tech billed 80 hours and clocked 160 total hours, time-on-property is 50 percent. The first tech is spending more paid time on property work.

Step three is to compare these metrics across territories to identify where work order volume is high relative to hours worked or low relative to hours worked. A territory with high work orders per day and low time-on-property suggests the tech is handling volume but spending paid time on non-property work. A territory with low work orders per day and high time-on-property suggests either low property demand or long wrench time per ticket.

Comparing territories with similar unit counts but different work order volumes reveals where property age or deferred maintenance drives workload beyond the ratio. If two territories each have 200 units but one generates 80 work orders per month and the other generates 40, the first territory needs more staffing or the second territory is over-staffed.

High work orders per day with low time-on-property suggests the tech is handling volume but spending paid time on non-property work. This could be training, travel between properties, administrative tasks, or time spent waiting for parts. The tech is productive when on property, but a large share of paid hours is not billed to properties.

Low work orders per day with high time-on-property suggests either low property demand or long wrench time per ticket. If the property is new or well-maintained, low work order volume is expected. If the property is older or has deferred maintenance, low work order volume with high time-on-property means the tech is spending a long time on each ticket. That could be appropriate for complex repairs or a sign the tech is stuck on work that should move faster.

Comparing territories with similar unit counts but different work order volumes reveals where property age or deferred maintenance drives workload beyond the ratio. A 200-unit property built in 1990 will generate more work orders than a 200-unit property built in 2015. The older property needs more staffing, even though the unit count is the same. Property maintenance staffing is fundamentally a data problem because the ratio hides these differences.

Territory comparison is the staffing decision tool. If one territory shows 4 work orders per day with 75 percent time-on-property and another shows 2 work orders per day with 60 percent time-on-property, the first territory is handling more volume more productively. If both territories have the same number of techs, the second territory is over-staffed or the first territory is under-staffed.

The current state is pulling reports from two systems, reconciling by hand, and updating calculations each time a staffing question arises. You run the work order report, run the payroll report, match the names, sum the hours, divide billable by total, and compare the results. The next time you need to check staffing, you repeat the process.

Continuous tracking means automated reconciliation on a schedule. The system pulls the work order report and the payroll report weekly, matches the techs, calculates the metrics, and updates the dashboard. Tracking maintenance metrics continuously lets you see staffing signals as they develop rather than after the fact.

The operational benefit is faster reallocation decisions when a territory's workload changes. If work order volume spikes in one territory and drops in another, you see it in the weekly update instead of waiting for the next manual report pull. You can move a tech from the low-volume territory to the high-volume territory before the backlog grows.

Continuous tracking does not replace manager judgment. The metrics tell you where to look, not what to do. A territory with low work orders per day might need more staffing if the property is about to start a major renovation. A territory with high time-on-property might be over-staffed if the property is about to lose half its units to a sale. The dashboard shows the pattern. The manager decides the action.